{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第一章作业"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "人工智能\n",
      "     机器学习\n",
      "     深度学习\n",
      "     计算机视觉\n",
      "编程语言\n",
      "     C/C++\n",
      "     Java\n",
      "     Python\n",
      "考试认证\n",
      "     华为认证\n",
      "     思科认证\n",
      "     软考\n",
      "移动开发\n",
      "     iOS\n",
      "     微信开发\n",
      "     Swift\n",
      "云计算/大数据\n",
      "     Hadoop\n",
      "     Docker/K8S\n",
      "     Spark\n",
      "其他课程\n",
      "     系统/网络/运维\n",
      "     数据库\n",
      "     区块链\n"
     ]
    }
   ],
   "source": [
    "import lxml.etree as le\n",
    "# 导入lxml模块的etree\n",
    "\n",
    "with open('edu.html','r',encoding='utf-8') as f:\n",
    "    html=f.read()\n",
    "    # print(html)\n",
    "    # 把字符串对象html转换成xpath对象\n",
    "    html_x=le.HTML(html)\n",
    "    # print(html_x)  # 得到xpath对象<Element html at 0x2070479bfc8>\n",
    "    div_x_s=html_x.xpath('//div[@class=\"classify_cList\"]')\n",
    "    data_s=[]\n",
    "    # print(div_x_s)  #得到xpath对象作为内容的列表[<Element div at 0x2a75c737208>, <Element div at 0x2a75c737448>, <Element div at 0x2a75c737f08>, <Element div at 0x2a75c737f48>, <Element div at 0x2a75c737f88>, <Element div at 0x2a75c740048>]\n",
    "    for div_X in div_x_s:\n",
    "        # 找到下面包裹的h3标签中的a标签中的文本即为1级目录,  . 表示当前路径\n",
    "        # category1=div_X.xpath('./h3/a/text()')\n",
    "        # print(category1)\n",
    "        category1 = div_X.xpath('./h3/a/text()')[0]\n",
    "        # print(category1)\n",
    "        category2_s=div_X.xpath('./div/span/a/text()')\n",
    "        # print(category2_s)\n",
    "        data_s.append(\n",
    "            dict(\n",
    "                category1=category1,\n",
    "                category2_s=category2_s\n",
    "            )\n",
    "        )\n",
    "    # print(data_s)\n",
    "    for data in data_s:\n",
    "        print(data.get('category1'))\n",
    "        # print(data.get('category2_s'))\n",
    "        for category2 in data.get('category2_s'):\n",
    "            print('    ',category2)"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第二章作业1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作业版本1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "请输入要翻译的内容:你好\n",
      "[nǐ hǎo] how do you do; how are you; hello;\n"
     ]
    }
   ],
   "source": [
    "import urllib.request as ur\n",
    "import urllib.parse as up\n",
    "import json\n",
    "\n",
    "# 由于是post请求并附带表单数据，所以需要放在request中的data\n",
    "word=input('请输入要翻译的内容:')\n",
    "\n",
    "data={\n",
    "    'kw':word\n",
    "}\n",
    "data_url=up.urlencode(data)\n",
    "# 通过encode把数据转化成字节类型\n",
    "request=ur.Request(\n",
    "    url='https://fanyi.baidu.com/sug',\n",
    "    data=data_url.encode('utf-8'),\n",
    ")\n",
    "\n",
    "# 返回字节类型数据response\n",
    "response=ur.urlopen(request).read()\n",
    "# print(response)\n",
    "\n",
    "# 利用json的loads对传回的数据进行转换，数据可以是bytes字节类型，也可以是字符串类型str，转换成python对象\n",
    "ret=json.loads(response)\n",
    "# print(ret)\n",
    "\n",
    "translate=ret['data'][0]['v']\n",
    "print(translate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作业版本2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "请输入要翻译的内容（非中文翻译成中文，中文翻译成英文）：python\n",
      "{'trans_result': {'data': [{'dst': '蟒蛇', 'prefixWrap': 0, 'result': [[0, '蟒蛇', ['0|6'], [], ['0|6'], ['0|6']]], 'src': 'python'}], 'from': 'en', 'status': 0, 'to': 'zh', 'type': 2, 'phonetic': [{'src_str': '蟒', 'trg_str': 'mǎng'}, {'src_str': '蛇', 'trg_str': 'shé'}]}, 'dict_result': {'edict': {'item': [{'tr_group': [{'tr': ['large Old World boas'], 'example': [], 'similar_word': []}, {'tr': ['a soothsaying spirit or a person who is possessed by such a spirit'], 'example': [], 'similar_word': []}], 'pos': 'noun'}], 'word': 'python'}, 'collins': {'entry': [{'type': 'mean', 'entry_id': '54938', 'value': [{'mean_type': [], 'gramarinfo': [], 'tran': '蟒蛇；蚺蛇；巨蛇', 'def': 'A <b>python</b> is a large snake that kills animals by squeezing them with its body.', 'mean_id': '52581', 'posp': [{'label': 'N-COUNT'}]}]}], 'word_name': 'python', 'word_id': '25401', 'word_emphasize': 'py|thon', 'frequence': '1'}, 'from': 'original', 'simple_means': {'word_name': 'python', 'from': 'original', 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     ]
    }
   ],
   "source": [
    "import urllib.request as ur\n",
    "import urllib.parse as up\n",
    "import json\n",
    "\n",
    "\n",
    "\n",
    "# 在使用百度实时翻译接口sug的时候出现了一个问题，对于用户输入复杂的语句，返回的data结果可能有异，可以加入try except关键字，即提升代码本身的健硕性\n",
    "\n",
    "# 输入要翻译的内容word\n",
    "def translate_input():\n",
    "    word = input(\"请输入要翻译的内容（非中文翻译成中文，中文翻译成英文）：\")\n",
    "    data = {\n",
    "        'query': word\n",
    "    }\n",
    "    return data,word\n",
    "\n",
    "# 通过输入的内容判断，内容所属语言\n",
    "def language_detect(data):\n",
    "    data_url = up.urlencode(data)\n",
    "    request = ur.Request(\n",
    "        url='https://fanyi.baidu.com/langdetect',\n",
    "        data=data_url.encode('utf-8'),\n",
    "    )\n",
    "\n",
    "    response = ur.urlopen(request).read()\n",
    "    # print(response)\n",
    "    ret = json.loads(response)\n",
    "    # print(ret)\n",
    "    translate = ret['lan']\n",
    "    # print(translate)\n",
    "    return translate\n",
    "\n",
    "# 得到要翻译的内容和所属语言，进行翻译，默认翻译方式：非中文翻译成中文，中文翻译成英文\n",
    "def translate(language,data,word):\n",
    "    # 'transtype': 'translang',\n",
    "    # 'transtype': 'realtime',\n",
    "    # 'simple_means_flag': '3',\n",
    "    # 'sign': '477811.239938',\n",
    "    # 'token': '364644cb52d71a13c96acbe7818896d1',\n",
    "    # 'domain': 'common',\n",
    "    data = {\n",
    "        'from': language,\n",
    "        'to': 'zh',\n",
    "        'query':word,\n",
    "        'sign': '477811.239938',\n",
    "        'token': '364644cb52d71a13c96acbe7818896d1',\n",
    "    }\n",
    "    data_url=up.urlencode(data)\n",
    "    # ur.Request()中的url原本想着是下面的这个，但实际总是返回'errno': 997, 'error': 997。所以修改成http://fanyi.baidu.com/transapi\n",
    "    # url='https://fanyi.baidu.com/v2transapi?from='+language+'&to=zh',\n",
    "    # url='http://fanyi.baidu.com/transapi',\n",
    "    # url='http://fanyi.baidu.com/basetrans',\n",
    "    request=ur.Request(\n",
    "        url='https://fanyi.baidu.com/v2transapi?from='+language+'&to=zh',\n",
    "        headers={\n",
    "            'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.138 Safari/537.36',\n",
    "            'Cookie':'BIDUPSID=B6349526E839181D05D388E6CC9DEEE1; PSTM=1577353092; BAIDUID=B6349526E839181D01E0B26715E40AAA:FG=1; MCITY=-%3A; BDUSS=BYYzZaamN-RENnR1A5cDVtSXhRRENJaktYOXpBeFFneDViWFRLMDdtLTdTY1plRVFBQUFBJCQAAAAAAAAAAAEAAACV7C4EZGttYWhhbwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAALu8nl67vJ5eUF; REALTIME_TRANS_SWITCH=1; FANYI_WORD_SWITCH=1; SOUND_SPD_SWITCH=1; HISTORY_SWITCH=1; SOUND_PREFER_SWITCH=1; BDRCVFR[Az4XYP37mB6]=mk3SLVN4HKm; H_PS_PSSID=; delPer=0; PSINO=2; BDORZ=FFFB88E999055A3F8A630C64834BD6D0; Hm_lvt_64ecd82404c51e03dc91cb9e8c025574=1589784190,1589784196,1589784458,1589785030; Hm_lpvt_64ecd82404c51e03dc91cb9e8c025574=1589791060; yjs_js_security_passport=0107ce90bcc855fe6e2af2fd8077f66f9d607868_1589791061_js',\n",
    "        },\n",
    "        data=data_url.encode('utf-8'),\n",
    "    )\n",
    "    response=ur.urlopen(request).read()\n",
    "    ret=json.loads(response)\n",
    "    print(ret)\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    data,word=translate_input()\n",
    "    language_from=language_detect(data)\n",
    "    translate(language_from,data,word)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第二个思路卡在sign和token这里，也不知道整体的思路对不对。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第二章作业2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "关键词:证件识别\n",
      "起始页:1\n",
      "结束页:3\n",
      "通过调用API在JavaWeb项目中实现证件识别\n",
      "云端证件识别\n",
      "OCR识别技术之—移动端证件识别\n",
      "IOS证件识别开发包\n",
      "移动端证件识别，支持离线识别\n",
      "手机端扫描证件识别SDK\n",
      "身份证证件识别接口编写的JAVA调用示例\n",
      "证件识别、图像识别API接口应用广泛\n",
      "OCR证件识别技术的功能特点\n",
      "移动端证件识别SDK\n",
      "移动端证件识别采用视频预览模式识别\n",
      "移动端证件识别支持证件种类多\n",
      "如何提高证件录入速度，证件识别技术带来亮点\n",
      "移动端的证件识别APP简介\n",
      "移动端证件识别智能图片文字提取\n",
      "移动端证件识别，OCR证件采集\n",
      "ocr证件识别技术的广泛应用\n",
      "移动端证件识别文字提取\n"
     ]
    }
   ],
   "source": [
    "import urllib.request as ur\n",
    "import urllib.parse as up\n",
    "import lxml.etree as le\n",
    "import re\n",
    "# https://so.csdn.net/so/search/s.do?p=3&q=orc&t=blog&viparticle=&domain=&o=&s=&u=&l=&f=&rbg=0\n",
    "# https://so.csdn.net/so/search/s.do?p=3&q=%E8%AF%81%E4%BB%B6%E8%AF%86%E5%88%AB&t=blog&viparticle=&domain=&o=&s=&u=&l=&f=&rbg=0\n",
    "# ?p=2&q=证件识别&t=&viparticle=&domain=&o=&s=&u=&l=&f\n",
    "url='https://so.csdn.net/so/search/s.do?p={page}&q={keyword}&t=blog&viparticle=&domain=&o=&s=&u=&l=&f=&rbg=0'\n",
    "\n",
    "# 获取url访问时返回得到的response\n",
    "# Use-Agent 能有最好就有，cookies能没有就没有\n",
    "def getResponse(url):\n",
    "    req=ur.Request(\n",
    "        url=url,\n",
    "        headers={\n",
    "            'Use-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.138 Safari/537.36',\n",
    "        }\n",
    "    )\n",
    "    response=ur.urlopen(req).read()\n",
    "    return response\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    keyword=input('关键词:')\n",
    "    # 通过parse中的quote对中文进行url编码，不然url传输中，编码内容就会出现错误\n",
    "    keyword=up.quote(keyword)\n",
    "    # print(keyword)\n",
    "    pn_start=int(input('起始页:'))\n",
    "    pn_end=int(input('结束页:'))\n",
    "\n",
    "    for page in range(pn_start,pn_end+1):\n",
    "        # 访问1级页面\n",
    "        response=getResponse(\n",
    "            url='https://so.csdn.net/so/search/s.do?p={page}&q={keyword}&t=blog&viparticle=&domain=&o=&s=&u=&l=&f=&rbg=0'.format(page=page,keyword=keyword)\n",
    "        )\n",
    "\n",
    "    # 二级页面，具体的博客的链接\n",
    "    # response是个字典对象，所以需要转换成xml对象\n",
    "    hrefs=le.HTML(response).xpath('//div[@class=\"search-list-con\"]/dl//span[@class=\"mr16\"]/../../dt/div/a[1]/@href')\n",
    "    for href in hrefs:\n",
    "        response_blog=getResponse(\n",
    "            url=href,\n",
    "        )\n",
    "        title=le.HTML(response_blog).xpath('//h1[@class=\"title-article\"]/text()')[0]\n",
    "        title = re.sub(\n",
    "            r'[/\\\\:*\"<>|?]', '', title\n",
    "        )\n",
    "        filepath='blog/%s.html'  %title\n",
    "        with open(filepath,'wb') as f:\n",
    "            f.write(response_blog)\n",
    "        print(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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